RT Conference Proceedings
T1 Real-Time odor classification through sequential bayesian filtering
A1 González-Monroy, Javier
A1 González-Jiménez, Antonio Javier
K1 Olores - Control - Automatización
AB The classification of volatiles substances with an e-nose is still a challenging problem, particularly when working under real-time, out-of-the-lab environmental conditions where thechaotic and highly dynamic characteristics of the gastransportation induce an almost permanent transient state in the e-nose readings. In this work, a sequential Bayesian filtering approach is proposed to efficiently integrate information from previous e-nose observations while updating the belief about the gas class on a real-time basis. We validate our proposal with tworeal olfaction datasets composed of dynamic time-series experiments (gas transitions are Considered, but no mixture of gases), showing an improvement in the classification rate when compared to a stand-alone probabilistic classifier.
YR 2015
FD 2015-07-08
LK http://hdl.handle.net/10630/10055
UL http://hdl.handle.net/10630/10055
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 25 feb 2026